How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.Comment: 10page

Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017

Representation learning on relational data

Humans utilize information about relationships or interactions between objects for orientation in various situations. For example, we trust our friend circle recommendations, become friends with the people we already have shared friends with, or adapt opinions as a result of interactions with other people. In many Machine Learning applications, we also know about relationships, which bear essential information for the use-case. Recommendations in social media, scene understanding in computer vision, or traffic prediction are few examples where relationships play a crucial role in the application. In this thesis, we introduce methods taking relationships into account and demonstrate their benefits for various problems. A large number of problems, where relationship information plays a central role, can be approached by modeling data by a graph structure and by task formulation as a prediction problem on the graph. In the first part of the thesis, we tackle the problem of node classification from various directions. We start with unsupervised learning approaches, which differ by assumptions they make about the relationship's meaning in the graph. For some applications such as social networks, it is a feasible assumption that densely connected nodes are similar. On the other hand, if we want to predict passenger traffic for the airport based on its flight connections, similar nodes are not necessarily positioned close to each other in the graph and more likely have comparable neighborhood patterns. Furthermore, we introduce novel methods for classification and regression in a semi-supervised setting, where labels of interest are known for a fraction of nodes. We use the known prediction targets and information about how nodes connect to learn the relationships' meaning and their effect on the final prediction. In the second part of the thesis, we deal with the problem of graph matching. Our first use-case is the alignment of different geographical maps, where the focus lies on the real-life setting. We introduce a robust method that can learn to ignore the noise in the data. Next, our focus moves to the field of Entity Alignment in different Knowledge Graphs. We analyze the process of manual data annotation and propose a setting and algorithms to accelerate this labor-intensive process. Furthermore, we point to the several shortcomings in the empirical evaluations, make several suggestions on how to improve it, and extensively analyze existing approaches for the task. The next part of the thesis is dedicated to the research direction dealing with automatic extraction and search of arguments, known as Argument Mining. We propose a novel approach for identifying arguments and demonstrate how it can make use of relational information. We apply our method to identify arguments in peer-reviews for scientific publications and show that arguments are essential for the decision process. Furthermore, we address the problem of argument search and introduce a novel approach that retrieves relevant and original arguments for the user's queries. Finally, we propose an approach for subspace clustering, which can deal with large datasets and assign new objects to the found clusters. Our method learns the relationships between objects and performs the clustering on the resulting graph

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal